🤖 AI Summary
This study addresses the challenge of accurately predicting mechanical properties—namely elongation, tensile strength, and yield strength—of alloy steels. We propose an ensemble learning prediction framework based on random forest regression. The model leverages chemical composition (Fe, Cr, Ni, Mn, Si, Cu, C, etc.) and cold-rolling reduction ratio as input features, integrated with systematic feature engineering, five-fold cross-validation, residual analysis, and learning curve diagnostics for robust modeling and optimization. Compared to conventional empirical formulas and single-model approaches, the proposed framework significantly enhances nonlinear relationship modeling capability and prediction robustness. On the test set, it achieves R² scores of 0.92–0.96 and reduces root-mean-square error (RMSE) by over 35%. These results demonstrate its practical utility in alloy design and process optimization, underscoring strong potential for industrial deployment.
📝 Abstract
This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science.